“New food cultures” and the absent food citizen: immigrants in urban food policy discourse
Bibliographic record
Abstract
Abstract Multicultural cities in the Global North are rapidly developing and releasing urban food policies that outline municipal visions of sustainable food systems. In turn, these policies shape conceptions of food citizenship in the city. While these policies largely absorb activities previously associated with “alternative” food systems, little is known about how they respond to critical food and race scholars who have noted that these food practices and spaces have historically marginalized immigrants. A critical discourse analysis of 22 urban food policies from Global North cities reveals that most policies do not meaningfully consider immigrant foodscapes, foodways, and food-related labour. Many promote hegemonic and/or ethno-nationalistic understandings of “healthy” and “sustainable” food without recognizing immigrants’ food-related knowledge and skills. Policies largely fail to connect the topic of immigrant labour with goals like shortening supply chains, subject immigrant neighbourhoods to stigmatizing health discourses, and lack acknowledgement of the barriers immigrants may face to participating in sustainable food systems. Relatedly, policy discourses articulate forms of food citizenship that emphasize individual obligations over rights related to food. This jeopardizes the potential for immigrants to be seen as belonging to dominant political urban food communities and benefitting from the symbolic and material rewards associated with them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.050 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".